评估生成音频系统及其参数

Ashvala Vinay, Alexander Lerch
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引用次数: 7

摘要

近年来,深度生成模型在音频合成方面取得了相当大的进展。然而,最先进的技术很难量化;不同的研究通常在报告结果时使用不同的评估方法和不同的度量标准,这使得与其他系统的直接比较即使不是不可能,也是困难的。此外,在大多数情况下,所报告的指标的感知相关性和意义是未知的,这就禁止了关于实际可用性和音频质量的任何结论性见解。本文提出了一项研究,研究了最先进的方法,并与(i)一组先前提出的音频重建客观指标,以及(ii)一项听力研究。结果表明,目前使用的客观指标不足以描述当前系统的感知质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating generative audio systems and their metrics
Recent years have seen considerable advances in audio synthesis with deep generative models. However, the state-of-the-art is very difficult to quantify; different studies often use different evaluation methodologies and different metrics when reporting results, making a direct comparison to other systems difficult if not impossible. Furthermore, the perceptual relevance and meaning of the reported metrics in most cases unknown, prohibiting any conclusive insights with respect to practical usability and audio quality. This paper presents a study that investigates state-of-the-art approaches side-by-side with (i) a set of previously proposed objective metrics for audio reconstruction, and with (ii) a listening study. The results indicate that currently used objective metrics are insufficient to describe the perceptual quality of current systems.
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